This past year we have continued to evaluate the neural characteristics, and associated behavioral consequences, of Autism Spectrum Disorders (ASD). Previous studies have suggested that ASD is associated with abnormal early brain growth. More recently, cross-sectional studies suggest that cortical development during adolescence/ adulthood might also be aberrant. However, longitudinal designs that follow the same individuals over time are needed to evaluate this possibility. To accomplish this we analyzed structural MRI images acquired at least two years apart from our high functioning ASD and typically developing control subjects. We found accelerated cortical thinning for the ASD group as compared to typically developing individuals, especially in left ventral temporal cortex. Importantly, this accelerated cortical thinning was associated with impairments in everyday functioning and other social symptoms. These findings of longitudinal change extend prior cross-sectional studies by demonstrating increased cortical thinning in specific brain regions in ASD subjects during adolescence, thereby providing further evidence for atypical cortical development beyond the early years in ASD. In another series of studies we explored the functional consequences of these structural changes using task-based fMRI. We found that the neural circuitry associated with perceiving and understanding social interactions showed a lack of category-selectivity in ASD. Specifically, brain regions, such as ventral temporal cortex, that typically respond when viewing social, compared to mechanical interactions, responded equally strong to both types of situations in our ASD subjects. This finding suggests that ASD may be characterized by deficient neural connectivity between brain regions comprising a specific processing network or circuit. Our lab, as well as many others, have documented deficient neural connectivity in the ASD brain via analyses of slowly fluctuating neural activity recorded while subjects rest quietly in the MRI scanner. However, the viability of this measure of neural connectivity as a diagnostic biomarker of ASD remains to be investigated. To address this important question we used a large number of machine learning classifiers to distinguish ASD from typically developing control subjects using resting-state fMRI data obtained from our own subjects (59 high functioning males with ASD and 59 age- and IQ-matched typically developing males) and an additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset for replication. High classification accuracy was achieved through several analysis methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed all findings based on measures of neural connectivity (peak accuracy 95.19%). Thus, while individuals can be classified as having ASD with statistically significant accuracy based on their resting-state scans alone, this method falls short of biomarker standards. Nevertheless, the connectivity measures provided further evidence that ASD is characterized by dysfunction of large-scale functional brain networks, particularly those involved in social information processing.